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Register a new userIdentification and removal of non-Gaussian noise transients for gravitational wave searches
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We present a new ${it{gating}}$ method to remove non-Gaussian noise transients in gravitational wave data. The method does not rely on any a-priori knowledge on the amplitude or duration of the transient events. In light of the character of the newly released LIGO O3a data, glitch-identification is particularly relevant for searches using this data. Our method preserves more data than previously achieved, while obtaining the same, if not higher, noise reduction. We achieve a $approx$ 2-fold reduction in zeroed-out data with respect to the gates released by LIGO on the O3a data. We describe the method and characterise its performance. While developed in the context of searches for continuous signals, this method can be used to prepare gravitational wave data for any search. As the cadence of compact binary inspiral detections increases and the lower noise level of the instruments unveils new glitches, excising disturbances effectively, precisely, and in a timely manner, becomes more important. Our method does this. We release the source code associated with this new technique and the gates for the newly released O3 data.
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We present an algorithm for the identification of transient noise artifacts (glitches) in cross-correlation searches for long O(10s) gravitational-wave transients. The algorithm utilizes the auto-power in each detector as a discriminator between well-behaved Gaussian noise (possibly including a gravitational-wave signal) and glitches. We test the algorithm with both Monte Carlo noise and time-shifted data from the LIGO S5 science run and find that it is effective at removing a significant fraction of glitches while keeping the vast majority (99.6%) of the data. Using an accretion disk instability signal model, we estimate that the algorithm is accidentally triggered at a rate of less than 10^-5% by realistic signals, and less than 3% even for exceptionally loud signals. We conclude that the algorithm is a safe and effective method for cleaning the cross-correlation data used in searches for long gravitational-wave transients.
The detection of gravitational waves from compact binary coalescence by Advanced LIGO and Advanced Virgo provides an opportunity to study the strong-field, highly-relativistic regime of gravity. Gravitational-wave tests of General Relativity (GR) typically assume Gaussian and stationary detector noise, thus do not account for non-Gaussian, transient noise features (glitches). We present the results obtained by performing parameterized gravitational-wave tests on simulated signals from binary-black-hole coalescence overlapped with three classes of frequently occurring instrumental glitches with distinctly different morphologies. We then review and apply three glitch mitigation methods and evaluate their effects on reducing false deviations from GR. By considering 9 cases of glitches overlapping with simulated signals, we show that the short-duration, broadband blip and tomte glitches under consideration introduce false violations of GR, and using an inpainting filter and glitch model subtraction can consistently eliminate such false violations without introducing additional effects.
The radio skies remain mostly unobserved when it comes to transient phenomena. The direct detection of gravitational waves will mark a major milestone of modern astronomy, as an entirely new window will open on the universe. Two apparently independent phenomena can be brought together in a coincident effort that has the potential to boost both searches. In this paper we will outline the scientific case that stands behind these future joint observations and will describe the methods that might be used to conduct the searches and analyze the data. The targeted sources are binary systems of compact objects, known to be strong candidate sources for gravitational waves. Detection of transients coincident in these two channels would be a significant smoking gun for first direct detection of gravitational waves, and would open up a new field for characterization of astrophysical transients involving massive compact objects.
Long-lived gravitational wave (GW) transients have received interest in the last decade, as the sensitivity of LIGO and Virgo increases. Such signals, lasting between 10 and 1000s, can come from a variety of sources, including accretion disk instabilities around black holes, binary neutron stars post-merger, core-collapse supernovae, non-axisymmetric deformations in isolated neutron stars, and magnetar giant flares. Given the large parameter space and the lack of precisely modeled waveforms, searches must rely on robust detection algorithms, which make few or no assumptions on the nature of the signal. Here we present a new data analysis pipeline to search for long-lived transient GW signals, based on an excess cross-power statistic computed over a network of detectors. It uses a hierarchical strategy that allows to estimate the background quickly and implements several features aimed to increase detection sensitivity by 30% for a wide range of signal morphology compared to an older implementation. We also report upper limits on the GW energy emitted from a search conducted with the pipeline for GW emission around a sample of nearby magnetar giant flares, and discuss detection potential of such sources with second and third-generation detectors.
Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.
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